from llama_index.core.schema import Document
import pprint
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.core import Settings

embed_model = OllamaEmbedding(
  model_name='milkey/dmeta-embedding-zh:f16',
  prompt='Llamas are members of the camelid family',
)

doc = Document(text='RAG是一种常见的大模型应用范式，它通过检索—排序—生成的方式生成文本。',metadata={'title':'RAG模型介绍','author':'llama-index'})
pprint.pprint(doc.dict())
